Answer the following questions and complete the exercises in
RMarkdown. Please embed all of your code and push your final work to
your repository. Your final lab report should be organized, clean, and
run free from errors. Remember, you must remove the # for
the included code chunks to run. Be sure to add your name to the author
header above. For any included plots, make sure they are clearly
labeled. You are free to use any plot type that you feel best
communicates the results of your analysis.
Make sure to use the formatting conventions of RMarkdown to make your report neat and clean!
library(tidyverse)
library(janitor)
library(ggmap)
library(leaflet)
register_stadiamaps("ece99a72-2ade-4e51-85ff-001ee7169fb5", write = FALSE)
For this homework, we will use the shark attack data to visualize where the attacks occurred. The data are from: State of California- Shark Incident Database.
sharks <- read_csv("data/SharkIncidents_1950_2022_220302.csv") %>%
clean_names() %>%
filter(longitude !="NA" & latitude !="NA") %>% # pulling out NA locations
mutate(longitude = as.numeric(longitude)) # converting longitude to numeric
summary(sharks)
## incident_num month day year
## Length:205 Min. : 1.000 Min. : 1.00 Min. :1950
## Class :character 1st Qu.: 6.000 1st Qu.: 7.00 1st Qu.:1984
## Mode :character Median : 8.000 Median :18.00 Median :2004
## Mean : 7.878 Mean :16.44 Mean :1997
## 3rd Qu.:10.000 3rd Qu.:25.00 3rd Qu.:2013
## Max. :12.000 Max. :31.00 Max. :2022
## time county location mode
## Length:205 Length:205 Length:205 Length:205
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## injury depth species comment
## Length:205 Length:205 Length:205 Length:205
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## longitude latitude confirmed_source wfl_case_number
## Min. :-124.7 Min. :32.59 Length:205 Length:205
## 1st Qu.:-123.1 1st Qu.:34.04 Class :character Class :character
## Median :-122.0 Median :36.70 Mode :character Mode :character
## Mean :-121.4 Mean :36.36
## 3rd Qu.:-119.6 3rd Qu.:38.18
## Max. :-117.1 Max. :41.56
map1 <- get_stadiamap(bbox = c(left = -125, bottom = 32, right = -114, top = 42), zoom = 6, maptype = "stamen_terrain")
## ℹ © Stadia Maps © Stamen Design © OpenMapTiles © OpenStreetMap contributors.
stamen in a terrain style projection
and display the map.ggmap(map1)
ggmap(map1)+
geom_point(data = sharks, aes(x = longitude, y = latitude), color = "blue", size = 0.5, alpha = 0.5)+
labs(title="Shark Attacks in California", x="Longitude", y="Latitude")
ggmap(map1)+
geom_point(data = sharks %>% filter(injury == "fatal"),
aes(x = longitude, y = latitude),
color = "red",
size = 0.5,
alpha = 0.5)+
labs(title="Fatal Shark Attacks in California", x="Longitude", y="Latitude")
leaflet. Also, color the points red.leaflet(sharks %>% filter(injury == "fatal")) %>%
addTiles() %>%
addCircleMarkers(
lng = ~longitude,
lat = ~latitude,
radius = 2,
stroke = FALSE,
fillOpacity = 0.7,
color = "red") %>%
fitBounds(lng1=-125, lat1=32, lng2=-114, lat2=42)
sharks %>%
group_by(county) %>%
summarise(count = n()) %>%
arrange(desc(count))
## # A tibble: 21 × 2
## county count
## <chr> <int>
## 1 San Diego 24
## 2 San Mateo 19
## 3 Santa Barbara 19
## 4 Humboldt 18
## 5 Marin 16
## 6 Monterey 16
## 7 Santa Cruz 15
## 8 Sonoma 15
## 9 San Luis Obispo 14
## 10 Los Angeles 9
## # ℹ 11 more rows
ggmap(map1)+
geom_point(data = sharks %>% filter(county == "San Diego") %>% distinct(longitude, latitude),
aes(x = longitude, y = latitude),
color = "blue",
size = 0.5,
alpha = 0.5)+
labs(title="Shark Attacks in San Diego County", x="Longitude", y="Latitude")
Please knit your work as an .html file and upload to Canvas. Homework is due before the start of the next lab. No late work is accepted. Make sure to use the formatting conventions of RMarkdown to make your report neat and clean!